Artificial intelligence to diagnose meniscus tears on MRI.

Journal: Diagnostic and interventional imaging
Published Date: Mar 28, 2019

Abstract

PURPOSE: The purpose of this study was to build and evaluate a high-performance algorithm to detect and characterize the presence of a meniscus tear on magnetic resonance imaging examination (MRI) of the knee.

Authors

  • V Roblot
    UMR-S970, Department of Radiology, Hôpital Européen Georges-Pompidou, Assistance Publique-Hôpitaux de Paris, Université Paris-Descartes, 75015 Paris, France. Electronic address: victoire.roblot@aphp.fr.
  • Y Giret
    CentraleSupélec, Université Paris Saclay, 91190 Gif-sur-Yvette, France; Foodvisor, 75011 Paris, France.
  • M Bou Antoun
    UMR-S970, Department of Radiology, Hôpital Européen Georges-Pompidou, Assistance Publique-Hôpitaux de Paris, Université Paris-Descartes, 75015 Paris, France.
  • C Morillot
    CentraleSupélec, Université Paris Saclay, 91190 Gif-sur-Yvette, France.
  • X Chassin
    CentraleSupélec, Université Paris Saclay, 91190 Gif-sur-Yvette, France.
  • A Cotten
    Department of Musculoskeletal Radiology, Lille University Hospital, 59037 Lille, France.
  • J Zerbib
    UMR-S970, Department of Radiology, Hôpital Européen Georges-Pompidou, Assistance Publique-Hôpitaux de Paris, Université Paris-Descartes, 75015 Paris, France.
  • L Fournier
    UMR-S970, Department of Radiology, Hôpital Européen Georges-Pompidou, Assistance Publique-Hôpitaux de Paris, Université Paris-Descartes, 75015 Paris, France; Laboratoire de Recherche en Imagerie, LRI, PARCC-HEGP, UMR 970, Inserm/université Paris Descartes, Sorbonne-Paris cité, 75015 Paris, France.

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